Our experimental results show that our method reaches state-of-the-art performance.
We also show that our method exceeds the state-of-the-art in pose estimation tasks with moderate amounts of occlusion.
Our approach does not require any post-processing, such as pose refinement by the iterative closest point (ICP) algorithm [1]}.
In practice, we adapt PointNet [2]} for the rotation regression task.
Our input is a point cloud with spatial and color information.
We use the geodesic distance between rotations as the loss function.
